Evidence map›Paper›PMID 42445160›Full record

SynthesisFrontiers in medicine2026

Electromyographic biofeedback-assisted pelvic floor muscle training for female stress urinary incontinence: a systematic review and meta-analysis.

Jie Liu, Yu Liu, Shen Liu, Jingwen Chen

Abstract readSystematic Review
In one paragraph

Synthesis in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jie Liu *Ganzhou Cancer Hospital, Gannan Medical University, Ganzhou, Jiangxi, China.
Yu Liu *The First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China.
Shen LiuGanzhou Cancer Hospital, Gannan Medical University, Ganzhou, Jiangxi, China.
Jingwen ChenGanzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, Jiangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Pelvic floor muscle training (PFMT) is the preferred initial therapy for female stress urinary incontinence (SUI); however, achieving proper muscle activation and sustaining adherence over time are often difficult. Electromyographic biofeedback (EMG-BF) may facilitate motor learning during PFMT; however, its added benefit over PFMT alone remains uncertain. Objective: We investigated the clinical effects of EMG-BF-supported PFMT vs. standard PFMT in women with SUI. Methods: This systematic review and meta-analysis followed PRISMA 2020 and a prospectively registered protocol (PROSPERO: CRD420261281430). PubMed, Embase, Web of Science, Cochrane Library, and Scopus were searched from inception to 9 January 2026. RCTs enrolling adult women with SUI and directly comparing EMG-BF-assisted PFMT vs. PFMT alone were included. Outcomes were pooled as standardized mean differences (SMDs) with 95% confidence intervals (CIs). Risk of bias was assessed using Risk of Bias 2 (RoB 2). Heterogeneity was quantified using Results: Eight RCTs ( Conclusions: The addition of EMG-BF to PFMT provides small but statistically significant improvements in symptoms and quality of life (QoL) compared with PFMT alone, with a greater benefit observed for pelvic floor muscle strength. This supports the use of select, rather than regular, use of EMG-BF-particularly in those who have difficulty recognizing the correct contraction of the PF, and/or need extra structure at the beginning.

Indexed as

electromyographic biofeedbackmeta-analysispelvic floor muscle trainingstress urinary incontinencewomen health

Identifiers

PMID42445160
PMCPMC13357155

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.